A Robust Risk Assessment Model for Stress Corrosion Cracking
Bibliographic record
Abstract
Stress Corrosion Cracking (SCC) is a time dependent mechanism. Three conditions are required at the same location for the formation of SCC namely, susceptible material, susceptible environment and sufficient stress. Pipe age, operating stress level and coating type are significant parameters in determining the susceptibility to near-neutral pH SCC; whereas, additional parameters such as operating temperature and distance from compressor station are considered for high pH SCC. Environmental conditions such as soil type, topography and drainage have also shown correlation to SCC susceptibility. Several integrity assessment methods can be used to identify SCC on pipeline including hydrostatic testing, in-line inspection (ILI), and direct assessment (DA). Because the occurrence of SCC is a complex phenomenon and it depends on many parameters, it is important to develop a risk assessment model that can systematically incorporate all relevant evidences of SCC in a sensible way. This paper presents a robust risk assessment model for SCC, which uses evidence from failure histories, observation from assessments (i.e., digs, pressure tests, and ILIs), and mechanistic understanding of SCC (i.e. susceptible coating, pipe material, stress level, soil properties, etc.). This risk model is transparent and updateable, which allows incorporation of new scientific learnings and findings of SCC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".